class_docstr: Mini-batch Gradient Descent algorithm.
  
  Parameters
  ----------
  batch_size : int or None
      Set up min-batch size. The ``None`` value will ensure that all data
      samples will be propagated through the network at once.
      Defaults to ``128``.
  
  {BaseOptimizer.Parameters}
  
  Attributes
  ----------
  {BaseOptimizer.Attributes}
  
  Methods
  -------
  {BaseOptimizer.Methods}
  
  Examples
  --------
  >>> import numpy as np
  >>> from neupy import algorithms
  >>> from neupy.algorithms import *
  >>>
  >>> x_train = np.array([[1, 2], [3, 4]])
  >>> y_train = np.array([[1], [0]])
  >>>
  >>> network = Input(2) >> Sigmoid(3) >> Sigmoid(1)
  >>> optimizer = algorithms.GradientDescent(network, batch_size=1)
  >>> optimizer.train(x_train, y_train)
docstr: Check the prediction error for the specified input samples
  and their targets.
  
  Parameters
  ----------
  X : array-like
  y : array-like
  
  Returns
  -------
  float
      Prediction error.
docstr_tok: ["Check","the","prediction","error","for","the","specified","input","samples","and","their","targets",".","Parameters","--","--","--","--","--","X",":","array-like","y",":","array-like","Returns","--","--","--","-","float","Prediction","error","."]
code: def score(self, X, y):
      """
      Check the prediction error for the specified input samples
      and their targets.
  
      Parameters
      ----------
      X : array-like
      y : array-like
  
      Returns
      -------
      float
          Prediction error.
      """
      X = self.format_input(X)
      y = self.format_target(y)
  
      return iters.apply_batches(
          function=self.functions.score,
          inputs=as_tuple(X, y),
          batch_size=self.batch_size,
          show_output=True,
          show_progressbar=self.logs.enable,
          average_outputs=True,
      )
  
imports: from __future__ import division
  import time
  import numpy
  import tensorflow
  from neupy import layers
  from neupy.core.properties import FunctionWithOptionsProperty, ScalarVariableProperty, IntProperty, Property
  from neupy.utils import AttributeKeyDict, format_data, as_tuple, iters, tf_utils
  from neupy.algorithms.gd import objectives
  from neupy.exceptions import InvalidConnection
  from neupy.algorithms.base import BaseNetwork
label_desc: Use image node_id 3 for calling the GradientDescent obj's underlying member method code with example usage: obj.score(X, y) and returns: iters